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| import gradio as gr | |
| import jax | |
| import jax.numpy as jnp | |
| import numpy as np | |
| from flax.jax_utils import replicate | |
| from flax.training.common_utils import shard | |
| from PIL import Image | |
| from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel | |
| import cv2 | |
| def create_key(seed=0): | |
| return jax.random.PRNGKey(seed) | |
| controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( | |
| "JFoz/dog-cat-pose", dtype=jnp.bfloat16 | |
| ) | |
| pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16 | |
| ) | |
| def infer(prompts, negative_prompts, image): | |
| params["controlnet"] = controlnet_params | |
| num_samples = 1 #jax.device_count() | |
| rng = create_key(0) | |
| rng = jax.random.split(rng, jax.device_count()) | |
| im = image | |
| image = Image.fromarray(im) | |
| prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples) | |
| negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples) | |
| processed_image = pipe.prepare_image_inputs([image] * num_samples) | |
| p_params = replicate(params) | |
| prompt_ids = shard(prompt_ids) | |
| negative_prompt_ids = shard(negative_prompt_ids) | |
| processed_image = shard(processed_image) | |
| output = pipe( | |
| prompt_ids=prompt_ids, | |
| image=processed_image, | |
| params=p_params, | |
| prng_seed=rng, | |
| num_inference_steps=50, | |
| neg_prompt_ids=negative_prompt_ids, | |
| jit=True, | |
| ).images | |
| output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:]))) | |
| return output_images | |
| #gr.Interface(infer, inputs=["text", "text", "image"], outputs="gallery").launch() | |
| title = "Animal Pose Control Net" | |
| description = "This is a demo of Animal Pose ControlNet, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning." | |
| #with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as demo: | |
| #gr.Markdown( | |
| # """ | |
| # Animal Pose Control Net | |
| # This is a demo of Animal Pose Control Net, which is a model trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. | |
| #""") | |
| theme = gr.themes.Default(primary_hue="green").set( | |
| button_primary_background_fill="*primary_200", | |
| button_primary_background_fill_hover="*primary_300", | |
| ) | |
| gr.Interface(fn = infer, inputs = ["text", "text", "image"], outputs = "gallery", | |
| title = title, description = description, theme='gradio/soft', | |
| examples=[["a Labrador crossing the road", "low quality", "pose_256.jpg"]] | |
| ).launch() | |
| gr.Markdown( | |
| """ | |
| * [Dataset](https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset) | |
| * [Diffusers model](), [Web UI model](https://huggingface.co/JFoz/dog-pose) | |
| * [Training Report](https://wandb.ai/john-fozard/dog-cat-pose/runs/kmwcvae5)) | |
| """) | |